1. Field of the Invention
This invention relates generally to communication systems, and, more particularly, to wireless communication systems.
2. Description of the Related Art
The coverage area of a wireless communication system is typically divided into a number of cells, which may be grouped into one or more networks. A base station (or alternatively a node-B, base station router, or access network) typically provides wireless connectivity to mobile units located in each cell. The mobile units may include devices such as mobile telephones, personal data assistants, smart phones, text messaging devices, Global Positioning System devices, wireless network interface cards, desktop or laptop computers, and the like. Mobile units located in each cell may access the wireless communications system by establishing a wireless communication link, often referred to as an air interface, with the base station that serves the cell.
The wireless communication link is formed using radio waves that may be transmitted between the mobile units and the base stations. Radio waves experience fading as they propagate from the transmitting end to the receiving end of the wireless communication link. Fading may be defined as a decrease in the intensity of a radio wave as it propagates from a transmitter to a receiver. The most common type of fading is Rayleigh fading, which is caused by scattering of the radio wave by scattering entities (or scatterers) in the environment. For example, Rayleigh fading may be significant in environments that have a large number or density of scatterers, such as the relatively dense distribution of buildings in urban environments. Rayleigh fading typically causes the transmitted radio wave to be received over a range of times, i.e., Rayleigh fading is a type of temporal fading. For example, portions of the radio wave that traveled directly from the transmitter to the receiver without being scattered may arrive at a time T1, whereas portions of the radio wave that have been scattered by a scatterer while traveling from the transmitter to the receiver may travel over a longer distance and therefore may arrive at a time T2>T1. Doppler shifting of the signal caused by the relative motion of the mobile unit and the scatterer or antenna may also shift the frequency (or wavelength) of the radio wave.
Rayleigh fading over a wireless communication link may be modeled using a one-dimensional model of a time series of the received signal. For example, the time series of the received signal may be modeled in terms of temporal correlations between portions of the time series, temporal averages of portions of the time series, temporal variances of portions of the time series, and other statistical measures. One exemplary model of Rayleigh fading is the well-known Jakes model, in which the time series is assumed to be generated by a one-dimensional angular distribution of scatterers surrounding the mobile transmitter or receiver. Various parameters of the one-dimensional distribution of scatterers may be varied to match the scattering environment, the speed of the transmitter and/or receiver, the deployment scenario for the transmitters and/or receivers, and other factors. For example, field tests may be used to define input parameters for a Jakes model of a particular environment, such as a neighborhood in an urban environment. The Jake's model may be used for testing, for performance analysis, to estimate capacities, to evaluate the performance of the wireless communication links, and the like in wireless communication systems, particularly those that include a single antenna for transmitting and receiving.
Base stations (and in some cases mobile units) may use more than one antenna for providing wireless connectivity within the cell coverage area. For example, a base station may use multiple antennas for transmitting information to mobile units over a forward link (or downlink) and receiving information from the mobile units over a reverse link (or uplink). Cell coverage and/or throughput can be improved by employing multiple antennas at either the transmitting or receiving end of the wireless communication link using techniques such as transmit and/or receive diversity, intelligent antennas, space-time coding, and the like. These techniques are conventionally referred to as Multiple-In-Multiple-Out (MIMO) technologies.
Temporal models like the Jakes model are able to capture the temporal statistics of the fading in multiple antenna systems, but these one-dimensional models are not able to capture spatial correlations or polarization correlations between the antennas. Two-dimensional models (such as a modified Jakes model) have been developed to model temporal and a portion of the spatial correlations in multiple antenna systems. The two-dimensional Jakes model specifies the geometric location of each scatter within a circular cloud around a mobile unit and then uses this two-dimensional scatter cloud to determine fading coefficients for each antenna. However, two-dimensional models are limited to modeling fading of a single polarization and therefore do not capture the polarization correlations between the multiple antennas. Consequently, these two-dimensional models may only be used to model fading in relatively simple multi-antenna systems, e.g., a four vertical-polarization configuration for 4-branch receiver diversity at the base station. More sophisticated two-dimensional channel models have been proposed but these models typically require extensive computation and take a long time to run. Furthermore, these models provide little or no intuition regarding the propagation physics.
Spatial channel modeling has also been hindered by a number of technical difficulties. For example, modeling spatial correlations requires expensive measurement data that is typically expensive and time-consuming to collect. Furthermore, spatial correlations may be highly dependent on the environment. For example, spatial correlations typically differ significantly between urban, suburban, and rural environments. The spatial correlations may also be different in macro-cell and micro-cell deployment scenarios. For example, the relatively large coverage areas and high vertical displacements of the base station antennas in macro-cell deployments may lead to high spatial correlations between the antennas. In contrast, base station antennas in micro-cell deployments are typically deployed much lower and in regions that may include a large number of scatterers, which may lead to a lower spatial correlation between the antennas. Antenna radiation patterns and the relative position of the mobile unit with respect to the antenna radiation orientation may also affect the spatial correlations. The difficulties are even more pronounced for models of polarization diversity.